Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

https://www.physionet.org/content/ecgiddb/1.0.0/

Graph and download economic data for Employment Cost Index: Total compensation for Private industry workers in All industries and occupations (CIU2010000000000I) from Q1 2001 to Q2 2025 about ECI, occupation, compensation, workers, private industries, private, industry, and USA.

This dataset is the chunked version for the competition of CMI Sleep State detection. This dataset is generated by this notebook. Please refer to this notebook to know how to load the data and other functionalities.

More detailed information on this data, please refer to the official dataset page.

Credits

1. Sleep State: Fast Data Access with Parquet by Tolga @tolgadincer for the parquet fast access tips

Data Dict

1. train_events.csv, same as in the official dataset page
2. train_events_summary.csv: summary of the train_events.csv
   • count_events: total count of events
   • count_non_null_events: total count of non-null events
   • pct_non_null_events: count_non_null_events divided by count_events
   • {min|max}_{onset|wakeup}_ts: earliest/latest timestamp of the corresponding event
3. train_events_grouped.csv: same with train_events.csv but instead each event is pivoted into columns
4. train_series_grouped.parquet: regrouped training set by series ID
5. train_series_grouped_by_subseries.parquet: regrouped training set by series ID and subseries ID (for batching)
6. train_series_grouped_summary.csv: summary of train_series_grouped_by_subseries.parquet (useful for batching)

License

This dataset aims for the easiness and higher memory-efficient loading purpose. The license is that of the same with original source of this data.

The Series and Class Report provides basic identification information for all active registered investment company series and classes have been issued IDs by the Commission.

Graph and download economic data for Consumer Price Index for All Urban Consumers: All Items in Size Class A (CUURA000SA0) from Dec 1986 to Sep 2025 about all items, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

Replication Data for: Consensus clustering for case series identification and adverse event profiles in pharmacovigilance This data was extracted from VigiBase, the WHO global database of individual case safety reports. Its reports are collected by national pharmacovigilance centres such as the US FDA, which are members of the WHO Programme for International Drug Monitoring. This study included data available in VigiBase on 27 December 2018, a total of 18.4 million reports, not counting suspected duplicates. This dataset includes data for three drugs, sumatriptan, ambroxol and tacrolimus (on reports in VigiBase drugs are encoded using the WHODrug dictionary for medicinal information). Data is provided in CSV files with header rows. Reports are encoded as sparse indices, where InstanceID[row_i] and AdverseEventID[row_i] mean that the Adverse Event ID given in row_i is present on the report with Instance ID given in row_i. Instance IDs and Adverse Event IDs are unique numbers and differ between the three drugs. On reports in VigiBase, adverse events are encoded in MedDRA®. In this dataset, 991 commonly reported MedDRA Preferred Terms are presented in full (in line with the Statement on MedDRA Data Sharing) whereas the rest are coded with the unique IDs, which differ between the drugs. MedDRA®, the Medical Dictionary for Regulatory Activities terminology, is the international medical terminology developed under the auspices of the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). MedDRA® trademark is registered by IFPMA on behalf of ICH.

Definition of data variables

Real output = LN(Gross Domestic Product/ PCE Deflator/ Population) * 100
Real consumption = LN((Personal Consumption Expenditures/ PCE Deflator) / Population) * 100 Real investment = LN((Private Non-Residential Investment/ PCE Deflator) / Population) * 100 Hours worked = LN((Average Weekly Hours * Employment/ 100)/ Population) * 100
Inflation = LN(PCE Deflator / PCE Deflator (-1) ) * 100 Real wage = LN(Hourly Compensation / PCE Deflator) * 100
Policy interest rate = Federal Funds Rate / 4 Relative price of investment = -1 * LN(Price Index of Private Non-Residential Investment/ PCE Deflator) *100

Source of the original data

Gross Domestic Product: Gross Domestic Product, Table 1.1.5. Gross Domestic Product, NIPA Source: U.S. Bureau of Economic Analysis

Personal Consumption Expenditures: Personal Consumption Expenditures, Table 1.1.5. Gross Domestic Product, NIPA Source: U.S. Bureau of Economic Analysis

Private Non-Residential Investment: Private Non-Residential Investment, Table 1.1.5 Gross Domestic Product, NIPA Source: U.S. Bureau of Economic Analysis

PCE Deflator: Personal Consumption Expenditures, Table 1.1.9. Implicit Price Deflator for Gross Domestic Product Source: U.S. Bureau of Economic Analysis

Price Index of Private Non-Residential Investment: Private Non-Residential Capital Formation, Deflator (PIB), OECD Economic Outlook Database Source: Organisation for Economic Co-Operation and Development

Population: Population level, Civilian Noninstitutional Population, 16 Years and Over, Labor Force Statistics from the Current Population Survey, Series ID = LNS10000000 Source: U.S. Bureau of Labor Statistics

(Period: 1947 – 1975) Population: Population level, Civilian Noninstitutional Population, 16 Years and Over, Labor Force Statistics from the Current Population Survey, Series ID = LNU00000000 Source: U.S. Bureau of Labor Statistics

Employment: Employment level, Employed, 16 Years and Over, All Industries, All Occupations, Labor Force Statistics from the Current Population Survey, Series ID = LNS12000000
Source: U.S. Bureau of Labor Statistics

Average Weekly Hours: Average Weekly Hours, Major Sector Productivity and Costs, Nonfarm Business, Series ID = PRS85006023
Source : U.S. Bureau of Labor Statistics

Hourly Compensation: Hourly Compensation, Major Sector Productivity and Costs, Nonfarm Business, Series ID = PRS85006103
Source : U.S. Bureau of Labor Statistics

Federal Funds Rate: Averages of Monthly Figures - Percent
Source: Board of Governors of the Federal Reserve System

Graph and download economic data for Producer Price Index by Commodity: Final Demand: Finished Goods (WPUFD49207) from Jan 1947 to Aug 2025 about finished, final demand, goods, commodities, PPI, inflation, price index, indexes, price, and USA.

At TaskMaster.Info, Karl Craven is “obsessively documenting the international Taskmaster franchise,” which began as a British game show on which comedians compete to win challenges such as watermelon speed-eating and high-fiving strangers. Reddit user Alohamori has used the site and other sources to create a “ridiculously comprehensive” database of that information, enabling queries such as the fastest-completed tasks, tasks awarding zero points, and episodes ending in ties

attempts id, task, contestant, PO, base, adjustment, points, rank, episode, series, team, location

3,925 rows

discrepancies id, contestant, task, episode, series, observed, official

10 rows

episode_scores id, episode, contestant, score, rank, series, srank

730 rows

episodes id, series, episode, title, winner, air_date, studio_date, points, tasks, finale, TMI

146 rows

intros id, series, clip, person, task

616 rows

measurements id, task, contestant, measurement, objective

2,017 rows

normalized_scores id, task, contestant, base, adjustment, points, rank, rigid, spread, scale, 5+3, 3+2, 3½+2½

3,925 rows

objectives id, unit, target, label

178 rows

people id, series, seat, name, dob, gender, hand, team, champion, TMI

112 rows

podcast id, episode, guest, topic, rating

148 rows

profanity id, series, episode, task, speaker, roots, quote, studio

2,010 rows

series id, name, episodes, champion, air_start, air_end, studio_start, studio_end, points, tasks, special, TMI

22 rows

series_scores id, series, contestant, score, rank

105 rows

special_locations id, name, latlong

28 rows

task_briefs id, task, brief

809 rows

task_readers id, task, reader, team, live

296 rows

task_winners id, task, winner, team, live

1,024 rows

tasks id, series, episode, summary, tags, location, points, std, TMI, YT

809 rows

tasks_by_objective id, task, objective

421 rows

team_tasks id, task, team, win

188 rows

teams id, series, members, size, initials, irregular

36 rows

title_coiners id, episode, coiner, task

146 rows

credits - Data is Plural

Energy System Time Series Suite - Data Archive

This archive contains variously sized sets of declustered time series within the context of energy systems. These series demonstrate low discrepancy and high heterogeneity in feature space, resulting in a roughly uniform distribution within this space.

For detailed information, please refer to the corresponding GitHub project:https://github.com/s-guenther/estss/

For associated research, seehttps://doi.org/10.1186/s42162-024-00304-8

Data is provided in .csv format. The GitHub project includes a Python function to load this data as a dictionary of pandas data frames.

Should you utilize this data, kindly also cite the associated research paper. For any queries, please feel free to reach out to us through GitHub or the contact details provided at the end of this readme file.

Folder Content

ts_*.csv: Contains declustered load profile time series in tabular format.

Size: (n+1) x (m+1), with n representing time steps (1000 per series) and m the number of series.

Includes a header row and index column. Headers indicate series id, and the index column numbers each time step, starting from 0.

The first half of the series (m/2) consistently display a constant sign (negative). They are sequentially numbered from 0.

The second half (m/2) display varying signs. Numbering starts from 1,000,000.

features_*.csv: Tabulates features corresponding to the time series.

Size: (m+1) x (f+1), where m is the number of time series and f is the number of features

Includes a header row and index column. Indexes represent time series id (matching ts_*.csv headers), and headers name the features.

norm_space_*.csv: Shows feature vectors in normalized feature space where time series are declustered. Provided for completeness; typically not needed by users.

Size: (m+1) x (g+1), where m is the number of timer series and g is the number of selected features space features. (a subset of f from features_*.csv).

Format matches features_*.csv.

info_*.csv: Maps declustered datasets to the manifolded dataset. Provided for completeness; typically not needed by users.

Size: (m+1) x 2, with m as series count. Columns contain manifolded set time series ids.

Includes an index column and a header. The index holds the remapped id of declustered series. Header 0 is non-significant.

Each ts_*.csv, features_*.csv, norm_space_*.csv, and info_*.csv file comes in four versions to accommodate various set sizes:

*_4096.csv

*_1024.csv

*_256.csv

*_64.csv

These represent sets with 4096, 1024, 256, and 64 time series, respectively,offering different densities in feature space population. The objective is to balance computational load and resolution for individual research needs.

Contact

ESTSS - Energy System Time Series SuiteCopyright (C) 2023Sebastian Günthersebastian.guenther@ifes.uni-hannover.de

Leibniz Universität HannoverInstitut für Elektrische EnergiesystemeFachgebiet für Elektrische Energiespeichersysteme

Leibniz University HannoverInstitute of Electric Power SystemsElectric Energy Storage Systems Section

https://www.ifes.uni-hannover.de/ees.html

United States CBO Projection:(CPI) Consumer Price IndexU: Annual data was reported at 325.772 1982-1984=100 in 2029. This records an increase from the previous number of 318.264 1982-1984=100 for 2028. United States CBO Projection:(CPI) Consumer Price IndexU: Annual data is updated yearly, averaging 263.055 1982-1984=100 from Dec 2011 (Median) to 2029, with 19 observations. The data reached an all-time high of 325.772 1982-1984=100 in 2029 and a record low of 224.960 1982-1984=100 in 2011. United States CBO Projection:(CPI) Consumer Price IndexU: Annual data remains active status in CEIC and is reported by Congressional Budget Office. The data is categorized under Global Database’s United States – Table US.I004: Consumer Price Index: Urban: Projection: Congressional Budget Office. Refer to Series ID 41060801 for the actual figures from the Bureau of Labor Statistics

Graph and download economic data for Consumer Price Index for All Urban Consumers: Medical Care Services in U.S. City Average (CUUR0000SAM2) from Mar 1935 to Sep 2025 about medical, urban, consumer, CPI, services, inflation, price index, indexes, price, and USA.

Hong Kong Consumer Price Index (B): YoY data was reported at 1.400 % in Dec 2016. This stayed constant from the previous number of 1.400 % for Nov 2016. Hong Kong Consumer Price Index (B): YoY data is updated monthly, averaging 4.600 % from Jul 1975 (Median) to Dec 2016, with 498 observations. The data reached an all-time high of 19.300 % in Feb 1980 and a record low of -6.900 % in Sep 1999. Hong Kong Consumer Price Index (B): YoY data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR – Table HK.I032: Consumer Price Index (B): 10/09-9/10=100. According to the source, Composite Consumer Price Index (B) of more than 1 decimal place are used in the calculation to derive the year-on-year rates of change. Furthermore, the year-on-year rates of change before October 2010 were derived using the index series in the base periods at that time (for instance the 2004/05-based index series), compared with the index a year earlier in the same base period. Rebased from Oct2009-Sep2010=100 to Oct2014-Sep2015=100 Replacement series ID: 376217877

About Dataset

Context

Scrapped from IMDb, the dataset is a collection of top 50,000 TV shows worldwide based on their popularity.

Content

The data contains 7 columns and 50,000 rows. 1. Series Title : The name of the TV show 2. Release Year : The Year the show was released in 3. Runtime : The runtime of single episode of the Show 4. Genre : The genre of the show 5. Rating : The rating the specific show has received from users in IMDB 6. Cast : The leading stars of the show 7. Synopsis : Background and summary of the story of the show

Inspiration

One of the most popular use of this dataset can be to create recommendation systems. The series can be categorized based on cast of your choice, rating and the type of genre you are into among others.

Acknowledgement

The dataset is prepared by scraping the IMDb's website but is not endorsed by IMDb.

Last working version, is v4 (not v5) available here.

Generation

This dataset is a result of an extensive scraping process that took approximately 10 hours. Each ID in the dataset was fetched from The Movie Database API, resulting in a collection of over 225,000 IDs.

To replicate the dataset, you can utilize my open-source NodeJS application available on GitHub. The application's source code is publicly accessible, allowing you to generate the same dataset on your own.

In case there is significant interest, there is a possibility of setting up an API that enables browsing through all the detailed information obtained from the scraping process. If you would like to express your interest or learn more, feel free to send a message via Reddit.

Missing data

Please note that due to limitations within The Movie Database API, certain data may be missing for some TV series. This primarily affects older and less well-known TV shows.

Contents

This dataset includes the following information for each entry:

• ID: Unique identifier for the TV series.
• Name: Title of the TV series.
• Original Name: Original title of the TV series.
• Overview: Brief summary or description of the TV series.
• Tagline: Catchphrase or memorable line associated with the TV series (if available).
• In Production: Indicates whether the TV series is currently in production or
• Status: Current status of the TV series (e.g., in production, ended, canceled),
• Original Language: Language in which the TV series was originally
• Origin Country: Country of origin for the TV
• Created By: Name(s) of the individual(s) or organization(s) credited with creating the TV
• First Air Date: Date when the TV series first aired.
• Last Air Date: Date of the latest episode or season finale.
• Number of Episodes: Total number of episodes available for the TV series.
• Number of Seasons: Total number of seasons produced for the TV series.
• Production Companies: Companies involved in the production of the TV series.
• Poster Path: URL of the poster image associated with the TV series. The format of the URL is as follows: https://www.themoviedb.org/t/p/w600_and_h900_bestv2/xxxx.jpg.
• Genres: Genre(s) or category to which the TV series belongs.
• Vote Average: Average rating given to the TV series by viewers.
• Vote Count: Total number of votes received for the TV series.
• Popularity: Popularity score or ranking of the TV series.

Please note that certain fields may be missing for some entries, particularly for older and less well-known TV series, due to limitations within The Movie Database API.

Composite CPI: sa: Electricity, gas and water data was reported at 107.000 Oct1999-Sep2000=100 in Dec 2006. This records a decrease from the previous number of 107.100 Oct1999-Sep2000=100 for Nov 2006. Composite CPI: sa: Electricity, gas and water data is updated monthly, averaging 101.200 Oct1999-Sep2000=100 from Oct 1999 (Median) to Dec 2006, with 87 observations. The data reached an all-time high of 112.900 Oct1999-Sep2000=100 in Sep 2006 and a record low of 76.900 Oct1999-Sep2000=100 in Jan 2003. Composite CPI: sa: Electricity, gas and water data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong – Table HK.I016: Composite Consumer Price Index: Seasonally Adjusted: 10/99-9/00=100. Rebased from Oct99-Sep00=100 to Oct04-Sep05=100. Replacement Series ID: 105111201

Hong Kong Consumer Price Index (C): YoY data was reported at 1.400 % in Dec 2016. This records an increase from the previous number of 1.300 % for Nov 2016. Hong Kong Consumer Price Index (C): YoY data is updated monthly, averaging 4.900 % from Jul 1975 (Median) to Dec 2016, with 498 observations. The data reached an all-time high of 17.600 % in Feb 1980 and a record low of -6.200 % in Jan 2000. Hong Kong Consumer Price Index (C): YoY data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR – Table HK.I043: Consumer Price Index (C): 10/09-9/10=100. According to the source, Composite Consumer Price Index (C) of more than 1 decimal place are used in the calculation to derive the year-on-year rates of change. Furthermore, the year-on-year rates of change before October 2010 were derived using the index series in the base periods at that time (for instance the 2004/05-based index series), compared with the index a year earlier in the same base period. Rebased from Oct2009-Sep2010=100 to Oct2014-Sep2015=100 Replacement series ID: 376217887

The Living Costs and Food Survey (LCFS) (and its predecessors the Expenditure and Food Survey (EFS) and the Family Expenditure Survey (FES)) is one of the longest time series of data on spending and demographics. Since it began, the survey has undergone many changes and this makes creating long time series of consistent variables difficult and time consuming. The Family Expenditure Survey and Living Costs and Food Survey Derived Variables, 1968-2017 study contains a consistent time series of expenditure and demographic variables from the FES, the EFS and the LCFS which are the result of a long history of work carried out at the Institute for Fiscal Studies since the 1980s.

Since then, these files have been maintained and added to, resulting in a rich set of data which can be used in a wide range of research. The code to derive the variables was written by a number of people over the years and in parts is a complex set of interconnecting units which would be very difficult to make public in any useful way in its entirety. This documentation takes the main bits of code and simplifies it to try to show how the data have been derived.

Latest edition information

For the second edition (September 2020), updated demographic data files (prefix 'fesdemo') were deposited with the number of rooms from 2006-2011 corrected (previously these had erroneously been recorded as zero). A new section has also been added to the user guide to provide additional help to users wishing to combine the 2006 derived data with the 2006 data from the main collection.

Hong Kong Consumer Price Index (A): YoY data was reported at 1.200 % in Dec 2016. This records a decrease from the previous number of 1.300 % for Nov 2016. Hong Kong Consumer Price Index (A): YoY data is updated monthly, averaging 4.700 % from Jul 1975 (Median) to Dec 2016, with 498 observations. The data reached an all-time high of 20.800 % in Feb 1980 and a record low of -6.200 % in Dec 2001. Hong Kong Consumer Price Index (A): YoY data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong – Table HK.I021: Consumer Price Index (A): 10/09-9/10=100. According to the source, Composite Consumer Price Index (A) of more than 1 decimal place are used in the calculation to derive the year-on-year rates of change. Furthermore, the year-on-year rates of change before October 2010 were derived using the index series in the base periods at that time (for instance the 2004/05-based index series), compared with the index a year earlier in the same base period. Rebased from Oct2009-Sep2010=100 to Oct2014-Sep2015=100 Replacement series ID: 376217867

Context

This dataset presents 10K time series of vegetation indexes obtained from the Moderate Resolution Imaging Spectroradiometer (MOD13Q1 product) and annotated with 20 land use/land cover classes obtained from the LEM Dataset.

This dataset was created with a focus on the Western Region of Bahia, Brazil. Region with relevant national agricultural production.

Content

This dataset consists of CSV files with the following columns: * id: time series identification; * date: date, in yyyy-MM-dd format, indicating the date of the MOD13Q1 product; * evi: value of the Enhanced vegetation index (EVI) on the date indicated; * class: land use / land cover class on the indicated date. - 0 - not identified - 1 - soybean - 2 - maize - 3 - cotton - 4 - coffee - 5- beans - 6 - wheat - 7 - sorghum - 8 - millet - 9 - eucalyptus - 10 - pasture - 11 - hay - 12 - grass - 13 - crotalari - 14 - maize+crotalari - 15 - cerrado - 16 - conversion area - 17 - uncultivated soil - 18 - ncc - 19 - brachiaria

Time series example: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1601740%2F0e709b5b22a66c48aa8bf4e73ed7260b%2FScreenshot%202021-01-03%20014842.png?generation=1609715067903154&alt=media" alt="">

Some images, in GeoTIFF format, have been added to illustrate the complete process of mapping land use using this dataset.

Acknowledgements

Huete, A., Justice, C., & Van Leeuwen, W. (1999). MODIS vegetation index (MOD13). Algorithm theoretical basis document, 3(213).

Sanches, I. D., Feitosa, R. Q., Montibeller, B., Diaz, P. A., Luiz, A. J. B., Soares, M. D., ... & Chamorro, J. (2020). First Results of the Lem Benchmark Database for Agricultural Applications. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 251-256

Kernel Starter

Kernel: Land use classification using Encoder-Decoder LSTM Results: https://media1.giphy.com/media/31QWFDvEhMqo8quD1v/giphy.gif" alt="">